Articles | Volume 19, issue 7
https://doi.org/10.5194/hess-19-3153-2015
https://doi.org/10.5194/hess-19-3153-2015
Research article
 | 
20 Jul 2015
Research article |  | 20 Jul 2015

Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework

M. S. Raleigh, J. D. Lundquist, and M. P. Clark

Related authors

Meteorological and evaluation datasets for snow modelling at 10 reference sites: description of in situ and bias-corrected reanalysis data
Cécile B. Ménard, Richard Essery, Alan Barr, Paul Bartlett, Jeff Derry, Marie Dumont, Charles Fierz, Hyungjun Kim, Anna Kontu, Yves Lejeune, Danny Marks, Masashi Niwano, Mark Raleigh, Libo Wang, and Nander Wever
Earth Syst. Sci. Data, 11, 865–880, https://doi.org/10.5194/essd-11-865-2019,https://doi.org/10.5194/essd-11-865-2019, 2019
Short summary
ESM-SnowMIP: assessing snow models and quantifying snow-related climate feedbacks
Gerhard Krinner, Chris Derksen, Richard Essery, Mark Flanner, Stefan Hagemann, Martyn Clark, Alex Hall, Helmut Rott, Claire Brutel-Vuilmet, Hyungjun Kim, Cécile B. Ménard, Lawrence Mudryk, Chad Thackeray, Libo Wang, Gabriele Arduini, Gianpaolo Balsamo, Paul Bartlett, Julia Boike, Aaron Boone, Frédérique Chéruy, Jeanne Colin, Matthias Cuntz, Yongjiu Dai, Bertrand Decharme, Jeff Derry, Agnès Ducharne, Emanuel Dutra, Xing Fang, Charles Fierz, Josephine Ghattas, Yeugeniy Gusev, Vanessa Haverd, Anna Kontu, Matthieu Lafaysse, Rachel Law, Dave Lawrence, Weiping Li, Thomas Marke, Danny Marks, Martin Ménégoz, Olga Nasonova, Tomoko Nitta, Masashi Niwano, John Pomeroy, Mark S. Raleigh, Gerd Schaedler, Vladimir Semenov, Tanya G. Smirnova, Tobias Stacke, Ulrich Strasser, Sean Svenson, Dmitry Turkov, Tao Wang, Nander Wever, Hua Yuan, Wenyan Zhou, and Dan Zhu
Geosci. Model Dev., 11, 5027–5049, https://doi.org/10.5194/gmd-11-5027-2018,https://doi.org/10.5194/gmd-11-5027-2018, 2018
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Uncertainty analysis
On the visual detection of non-natural records in streamflow time series: challenges and impacts
Laurent Strohmenger, Eric Sauquet, Claire Bernard, Jérémie Bonneau, Flora Branger, Amélie Bresson, Pierre Brigode, Rémy Buzier, Olivier Delaigue, Alexandre Devers, Guillaume Evin, Maïté Fournier, Shu-Chen Hsu, Sandra Lanini, Alban de Lavenne, Thibault Lemaitre-Basset, Claire Magand, Guilherme Mendoza Guimarães, Max Mentha, Simon Munier, Charles Perrin, Tristan Podechard, Léo Rouchy, Malak Sadki, Myriam Soutif-Bellenger, François Tilmant, Yves Tramblay, Anne-Lise Véron, Jean-Philippe Vidal, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023,https://doi.org/10.5194/hess-27-3375-2023, 2023
Short summary
Historical rainfall data in northern Italy predict larger meteorological drought hazard than climate projections
Rui Guo and Alberto Montanari
Hydrol. Earth Syst. Sci., 27, 2847–2863, https://doi.org/10.5194/hess-27-2847-2023,https://doi.org/10.5194/hess-27-2847-2023, 2023
Short summary
Daytime-only mean data enhance understanding of land–atmosphere coupling
Zun Yin, Kirsten L. Findell, Paul Dirmeyer, Elena Shevliakova, Sergey Malyshev, Khaled Ghannam, Nina Raoult, and Zhihong Tan
Hydrol. Earth Syst. Sci., 27, 861–872, https://doi.org/10.5194/hess-27-861-2023,https://doi.org/10.5194/hess-27-861-2023, 2023
Short summary
Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning
Lei Xu, Nengcheng Chen, Chao Yang, Hongchu Yu, and Zeqiang Chen
Hydrol. Earth Syst. Sci., 26, 2923–2938, https://doi.org/10.5194/hess-26-2923-2022,https://doi.org/10.5194/hess-26-2923-2022, 2022
Short summary
Unraveling the contribution of potential evaporation formulation to uncertainty under climate change
Thibault Lemaitre-Basset, Ludovic Oudin, Guillaume Thirel, and Lila Collet
Hydrol. Earth Syst. Sci., 26, 2147–2159, https://doi.org/10.5194/hess-26-2147-2022,https://doi.org/10.5194/hess-26-2147-2022, 2022
Short summary

Cited articles

Archer, G. E. B., Saltelli, A., and Sobol, I. M.: Sensitivity measures,anova-like Techniques and the use of bootstrap, J. Stat. Comput. Simul., 58, 99–120, https://doi.org/10.1080/00949659708811825, 1997.
Bales, R. C., Molotch, N. P., Painter, T. H., Dettinger, M. D., Rice, R., and Dozier, J.: Mountain hydrology of the western United States, Water Resour. Res., 42, W08432, https://doi.org/10.1029/2005WR004387, 2006.
Barnett, T. P., Pierce, D. W., Hidalgo, H. G., Bonfils, C., Santer, B. D., Das, T., Bala, G., Wood, A. W., Nozawa, T., Mirin, A. A., Cayan, D. R., and Dettinger, M. D.: Human-induced changes in the hydrology of the western United States, Science, 319, 1080–1083, https://doi.org/10.1126/science.1152538, 2008.
Baroni, G. and Tarantola, S.: A General Probabilistic Framework for uncertainty and global sensitivity analysis of deterministic models: A hydrological case study, Environ. Model. Softw., 51, 26–34, https://doi.org/10.1016/j.envsoft.2013.09.022, 2014.
Bastola, S., Murphy, C., and Sweeney, J.: The role of hydrological modelling uncertainties in climate change impact assessments of Irish river catchments, Adv. Water Resour., 34, 562–576, https://doi.org/10.1016/j.advwatres.2011.01.008, 2011.
Download
Short summary
A sensitivity analysis is used to examine how error characteristics (type, distributions, and magnitudes) in meteorological forcing data impact outputs from a physics-based snow model in four climates. Bias and error magnitudes were key factors in model sensitivity and precipitation bias often dominated. However, the relative importance of forcings depended somewhat on the selected model output. Forcing uncertainty was comparable to model structural uncertainty as found in other studies.